A Novel Two-Stage Model Based SCA against secAES

secAES v2 is a mask scheme for AES and it is implemented with affine and shuffle techniques. Existing attack against secAES uses template attack, which first locates multiplicative leakage points and establishes templates to recover the two byte random mask, and then recovers secret key with template attack. Template construction can be complex in practice, and locating leakage points can also be difficult. Therefore, template attack may not perform well if conditions are not satisfied. In order to optimize the attack efficiency against the secAES mask scheme, a two-stage side-channel attack is proposed. First, a dense connected network DenseNet-121 is established to learn the multiplicative leakage characteristics according to traces from secAES’s loadAndMaskInput function, and one byte multiplicative mask used can be recovered with a high probability. Second, based on the recovered multiplicative mask, key recovery is achieved using deep-learning side-channel attacks. The experimental results in simulation scenario show that compared with template attack, the proposed method does not need to find leakage points in the process of recovering one byte multiplicative mask. Furthermore, it is easier to implement, has lower computational complexity and has a higher success rate. Indeed, the DenseNet-121 network can be used to recover the multiplicative mask used by each plaintext block with a probability of 84%. During key recovery, based on the DenseNet-121 model, the required number of traces is reduced, only 4 traces are required to recover key bytes.